26,195 research outputs found
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Recommended from our members
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 2
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles
With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria
An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification
Simulation-based verification is beneficial for assessing otherwise dangerous
or costly on-road testing of autonomous vehicles (AV). This paper addresses the
challenge of efficiently generating effective tests for simulation-based AV
verification using software testing agents. The multi-agent system (MAS)
programming paradigm offers rational agency, causality and strategic planning
between multiple agents. We exploit these aspects for test generation, focusing
in particular on the generation of tests that trigger the precondition of an
assertion. On the example of a key assertion we show that, by encoding a
variety of different behaviours respondent to the agent's perceptions of the
test environment, the agency-directed approach generates twice as many
effective tests than pseudo-random test generation, while being both efficient
and robust. Moreover, agents can be encoded to behave naturally without
compromising the effectiveness of test generation. Our results suggest that
generating tests using agency-directed testing significantly improves upon
random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure
Automatic Generation of Road Geometries to Create Challenging Scenarios for Automated Vehicles Based on the Sensor Setup
For the offline safety assessment of automated vehicles, the most challenging
and critical scenarios must be identified efficiently. Therefore, we present a
new approach to define challenging scenarios based on a sensor setup model of
the ego-vehicle. First, a static optimal approaching path of a road user to the
ego-vehicle is calculated using an A* algorithm. We consider a poor perception
of the road user by the automated vehicle as optimal, because we want to define
scenarios that are as critical as possible. The path is then transferred to a
dynamic scenario, where the trajectory of the road user and the road layout are
determined. The result is an optimal road geometry, so that the ego-vehicle can
perceive an approaching object as poorly as possible. The focus of our work is
on the highway as the Operational Design Domain (ODD).Comment: Accepted at the 2020 IEEE Intelligent Vehicles Symposium (IV),
October 20-23, 202
- …